The ability to automatically determine the hierarchy of a utility monitoring system is important to the next generation of energy solutions. It is useful not only for producing one-line diagrams of how monitoring devices in the utility monitoring systems are spatially related in a hierarchical arrangement, but those one-line diagrams provide a spatial context to the data taken from all capable monitoring devices.
U.S. Pat. No. 7,272,518, entitled “Automated Hierarchy Classification In Utility Monitoring Systems,” discloses an “auto-learned hierarchy algorithm,” which automatically determines how monitoring devices are arranged in a utility monitoring system. This auto-learned hierarchy algorithm works well for utility monitoring systems in which all or most of the loads are being monitored by a monitoring device and the loads being monitored in the utility monitoring system experience a range of variations in the characteristic being monitored (such as power). This is because the auto-learned hierarchy algorithm exploits a correlation algorithm for correlating monitored data sets for any given pair of monitoring devices. The greater the range in variations seen in the monitored device data, the easier it is to determine whether a pair of devices correlates with one another and are determined to be linked in the monitoring system.
However, some utility monitoring system configurations can include loads that do not experience significant variations in their monitored characteristic (such as the rate of energy consumption when the utility monitoring system is a power monitoring system), or include large unmetered loads in the hierarchy that have a significant impact on the associated parent devices (if any). Placing monitoring devices into the hierarchical layout of such system configurations is generally more challenging. Moreover, variations in the monitoring devices themselves, which can be of different types and made by different manufacturers, render the hierarchical determination more complex because different devices can have different levels of accuracy of measurement.
For example, suppose a load consumes 1000 kW of power, but only 10 kW of that power is being monitored by a power meter in the power monitoring system. 990 kW of the power consumed by the load is unmonitored. Power data between a power meter monitoring that load and its parent are weakly correlated because the 10 kW power contribution is dwarfed by the large unmetered 990 kW load.